Fusion Assessment Comparison in Cervical Fusion Surgery

Anterior cervical discectomy and fusion (ACDF) and posterior cervical fusion (PCF) are well-established surgical management options for degenerative cervical disc disease.[1] Their basis as standard-of-care has relied heavily upon the description of clinical outcome measures. While patient satisfaction and neurological status are highly important, assessing the procedure’s mechanical success via radiographic evaluation is necessary to guide future management. However, despite the expansive literature available, assessing the success of cervical fusion has remained challenging. Although the studies analyzing the efficacy of ACDF and PCF provide radiographic analysis of fusion, they are handicapped by the lack of standardization resulting from variations in imaging techniques and definitions of successful arthrodesis.[2–4] These variations have resulted in inconsistency in reported outcomes of radiographic analysis, differences in patient management, and the inability to determine which assessment method is most accurate, possibly limiting the ability of patients to receive standardized care across providers.

In recent years, the expanding literature discussing various criteria, modalities, and recent advancements in artificial intelligence have only made the need for standardization even more necessary. In this review, we examine and compare the various leading methodologies and criteria for evaluating cervical fusion success through radiographic techniques and explore what the future may hold for developing a gold standard.

What are the current leading radiographic methods for assessing cervical fusion?

The most utilized radiographic methods for assessing cervical fusion are dynamic radiographs and computed tomography (CT). Static radiographs have been utilized as an initial approach to assess ACDF status, but they correlate only between 43% and 82% with pseudoarthrosis detected via operative exploration.[5] Dynamic radiographs, utilizing flexion and extension lateral x-ray imaging, have been shown to be more reliable given their ability to evaluate interspinous motion to assess stability of the fusion site.[5] The closest to a “gold standard” technique is advanced imaging, specifically CT, as magnetic resonance imaging (MRI) is limited due to magnetic susceptibility artifacts that can obscure the evaluation of bone integrity.[4,6,7]

Many studies utilize CT as their gold standard when assessing the accuracy of dynamic radiographs.[4,5] However, first-line use of CT is not recommended because of its higher radiation doses compared to conventional radiographs and potential for metal artifacts to obscure findings.[4]

Dynamic Radiography

Recent studies have focused on establishing criteria for the use of dynamic radiographs.[4,5] Several systematic reviews, including one by Rhee et al, recommend dynamic flexion-extension radiographs as the first-line imaging for ACDF assessment. Regarding PCF, the same study offers data to suggest static radiographs can be beneficial for assessing fusion; however, there were insufficient data to suggest whether it was superior to dynamic radiographs.[4] It has been shown that the reliability of dynamic radiographs may be compromised by several factors, including debate regarding the acceptable measurement parameters and thresholds for motion, variability in the subjective measurement and analysis of images, and variability in the position of the patient with the x-ray machine.[3,8,9]

The parameters in dynamic radiographs are Cobb angle change (CAC) and interspinous process movement (ISM).[4,5,7] CAC is measured as the difference in the angle obtained from the segmental Cobb angle of the fused vertebrae. ISM is defined as the difference in distances between the tips of spinous processes on flexion-extension radiographs. Articles described CAC thresholds ranging from >1° to >4°, while studies involving ISM recommended thresholds ranging from 0 mm to 2 mm of movement between flexion-extension radiographs. Cannada et al attempted to validate these parameters with surgical exploration and found that a CAC of 2° had a sensitivity of 82% and specificity of 39%, and Pearson correlation with pseudoarthrosis of 0.28.[10] ISM with a threshold of >2 mm of motion had greater sensitivity (91%) and specificity (89%) than CAC and a Pearson correlation with pseudoarthrosis of 0.77.10 Similarly, Song et al validated the use of ISM <1 mm as most accurate when compared with intraoperative exploration (κ = 0.725).[11]

Interobserver error is a challenging part of dynamic x-ray assessment. Studies have shown that due to the variance in assessment methodology and reliance on subjective assessment, surgeons may interpret results differently based on their experience and biases.[4,8,9,11] For example, a study by Skolasky et al found confirmation bias with higher fusion rates reported by the operating surgeon compared to an independent panel when assessing single level ACDF.[12] To address interobserver reliability, Song et al evaluated ISM measurement at 150%, 25%, and 100% magnification and found both increased inter- and intraobserver reliability with a correlation coefficient of 0.796 at 150% magnification.[11] Recent meta-analyses and systematic reviews have supported the described results and concluded ISM is superior to Cobb angle change and recommend viewing images at 150% magnification with thresholds of <1 mm of ISM alongside >4 mm of superjacent interspinous motion as confirming fusion.[4,5,9]

Additionally, patient positioning can alter radiographic imaging results. Pinter et al found that small isolated and combined changes in patient position and angle with the x-ray generator can influence the parameters, including the interspinous process distance, producing variability in imaging results.[8] The authors concluded that dynamic radiographs are unreliable for assessing arthrodesis and emphasized the need for additional standardization measures to eliminate or significantly reduce variability. While several researchers have questioned the reliability of dynamic radiographs,[4,5,7] the integration of advanced imaging modalities with artificial intelligence and machine learning is expected to improve accuracy and interrater reliability.

CT Imaging

CT demonstrates improved accuracy at identifying pseudoarthrosis due to improved interobserver reliability over standard radiographic methods and its ability to detail bridging trabecular bone directly at the fusion site. Despite its superiority, its use is only recommended after ambiguous radiographic findings, which have been described as interobserver variability, hardware artifacts, presence of radiolucent lines, and measurements that are close to the threshold value.[5,8,13]

There are several radiographic parameters for CT-based fusion diagnosis described in the recent literature ranging from vague descriptions of fusion status to more specific measurement-based values. Parameters include the presence of bone bridging and lack of bony lucency at the graft/vertebral body junction and can be measured differently depending on the study. For example, Kim et al defined bony fusion as “fused with remodeling and trabeculae present” or “graft intact, not fully remodeled and incorporated, but no lucency present,”[14] while others utilized more specific values, and some even calculated via multiple parameters. [4,5,15,16]

Current literature and most recent systematic reviews identify the extragraft bone bridging (ExGBB) method from Song et al as the most accurate at assessing pseudarthrosis, which is defined as any peripheral bone bridging with no lucent lines crossing the peripheral margins of the operated disc space outside the graft or cage (Figure).[4,5,15,16] Detection of ExGBB was obtained via multiple observers and was validated through surgical exploration of cases. ExGBB was found to have a high sensitivity (98.7%), specificity (92.1%), positive predictive value (92.1%), negative predictive value (98.7%), and interobserver reliability.[16] While CT is superior to standard radiography, it is still limited to images obtained in a single moment of time and is therefore unable to assess for changes in the spine during motion, limiting its ability to reliability in cases of nonunion or pseudoarthrosis only seen with movement.[5]

What emerging techniques will increase the accuracy of assessing cervical fusion?

Over the past decade, artificial intelligence and machine learning have been applied to the assessment of radiographic parameters and images throughout the medical field. More recently, they have been applied to improve the accuracy of cervical fusion assessments.[13] Two recent studies discuss the use of a convolutional neural network model to assess cervical fusion parameters following ACDF using dynamic imaging. Park et al created a model that utilized extracted radiographs in 3 positions (flexion, extension, and neutral) of only the fusion segment and close periphery to output a final decision of fusion or nonunion. The model had an accuracy of 89.5% when compared to observations utilizing both dynamic radiographs and CT images by orthopedic surgeons.[13]

More recently, a model described by Ham et al was able to detect spinous processes in radiographs with 99.2% accuracy (sensitivity 97.0%, precision 97.4%) and then measure the interspinous distance within flexion-extension paired radiographs to calculate ISM.[17] Compared to human observers, the model obtained a Cohen’s kappa of 0.82 when detecting the presence of segmental motion, indicating a strong level of agreement.[17]

Radioisometric analysis (RSA) is another method of assessing cervical fusion but has remained mostly experimental thus far.[7] A study by Parashin et al attempted to assess the accuracy, precision, and feasibility of RSA in PCF and posterior lumbar fusion procedures using artificial and cadaveric spine models with implanted beads.[18] They found high accuracy and detectability of the beads in cervical and lumbar segments with lower precision in the cervical compared to lumbar region. This study suggests RSA is feasible in the application of cervical spinal fusion and recommends continued investigation into the use of RSA for spinal fusion assessment.[18]

Conclusion

The definition and description of a gold standard in cervical spinal fusion through radiographic analysis is still an ongoing debate. The information in the present article is intended to present an overview of techniques so that readers may better understand what the current literature suggests is most valid. While there is still no standardized method of assessing cervical fusion, future studies will only help further define a gold standard.

References

1. Buttermann GR. Anterior cervical discectomy and fusion outcomes over 10 years: a prospective study. Spine. 2018;43(3):207-214.

2. Balouch E, Burapachaisri A, Woo D, et al. Assessing postoperative pseudarthrosis in anterior cervical discectomy and fusion (ACDF) on dynamic radiographs using novel angular measurements. Spine. 2022;47(16):1151-1156.

3. Noordhoek I, Koning MT, Vleggeert-Lankamp CLA. Evaluation of bony fusion after anterior cervical discectomy: a systematic literature review. Eur Spine J. 2019;28(2):386-399.

4. Rhee JM, Chapman JR, Norvell DC, Smith J, Sherry NA, Riew KD. Radiological determination of postoperative cervical fusion: a systematic review. Spine. 2015;40(13):974-991.

5. Lin W, Ha A, Boddapati V, Yuan W, Riew KD. Diagnosing pseudoarthrosis after anterior cervical discectomy and fusion. Neurospine. 2018;15(3):194-205.

6. Godlewski B, Bebenek A, Dominiak M, Bochniak M, Cieslik P, Pawelczyk T. Reliability and utility of various methods for evaluation of bone union after anterior cervical discectomy and fusion. J Clin Med. 2022;11(20):6066.

7. Selby MD, Clark SR, Hall DJ, Freeman BJC. Radiologic assessment of spinal fusion. J Am Acad Orthop Surg. 2012;20(11):694-703.

8. Pinter ZW, Skjaerlund J, Michalopoulos GD, et al. Dynamic radiographs are unreliable to assess arthrodesis following cervical fusion: a modeled radiostereometric analysis of cervical motion. Spine (Phila Pa 1976). 2023;48(2):127-136.

9. Oshina M, Oshima Y, Tanaka S, Riew KD. Radiological fusion criteria of postoperative anterior cervical discectomy and fusion: a systematic review. Glob Spine J. 2018;8(7):739-750.

10. Cannada LK, Scherping SC, Yoo JU, Jones PK, Emery SE. Pseudoarthrosis of the cervical spine: a comparison of radiographic diagnostic measures. Spine (Phila Pa 1976). 2003;28(1):46-51.

11. Song KS, Piyaskulkaew C, Chuntarapas T, et al. Dynamic radiographic criteria for detecting pseudarthrosis following anterior cervical arthrodesis. J Bone Joint Surg Am. 2014;96(7):557-563.

12. Skolasky RL, Maggard AM, Hilibrand AS, et al. Agreement between surgeons and an independent panel with respect to surgical site fusion after single-level anterior cervical spine surgery: a prospective, multicenter study. Spine. 2006;31(15):E503-E506.

13. Park S, Kim JK, Chang MC, Park JJ, Yang JJ, Lee GW. Assessment of fusion after anterior cervical discectomy and fusion using convolutional neural network algorithm. Spine. 2022;47(23):1645-1650.

14. Kim CH, Chung CK, Hahn S. Autologous iliac bone graft with anterior plating is advantageous over the stand-alone cage for segmental lordosis in single-level cervical disc disease. Neurosurgery. 2013;72(2):257-265; discussion 266.

15. Riew KD, Yang JJ, Chang DG, et al. What is the most accurate radiographic criterion to determine anterior cervical fusion? Spine J. 2019;19(3):469-475.

16. Song KS, Chaiwat P, Kim HJ, Mesfin A, Park SM, Riew KD. Anterior cervical fusion assessment using reconstructed computed tomographic scans: surgical confirmation of 254 segments. Spine (Phila Pa 1976). 2013;38(25):2171-2177.

17. Ham DW, Choi YS, Yoo Y, Park SM, Song KS. Measurement of interspinous motion in dynamic cervical radiographs using a deep learning-based segmentation model. J Neurosurg Spine. 2023;39(3):329-334.

18. Parashin S, Gascoyne T, Zarrabian M. A phantom and cadaveric study of radiostereometric analysis in posterior cervical and lumbar spinal fusion. Spine J. 2020;20(8):1333-1343.

Contributors:

Adin M. Ehrlich, BA[1]

Tomoyuki Asada, MD[1]

Sheeraz A. Qureshi, MD[1,2]

From the [1]Hospital for Special Surgery and [2]Weill Cornell Medical College, both in New York, New York.